This is the RMarkdown file for the 3Soils experiment. Tables and figures for molecular results. For formatted tables, see the Word document titled 000-3soils_markdown_YYYYMMDD.docx.

Run: 2020-04-21

Soil characterization

summary table

variable CPCRW DWP SR
TC_perc 1.43 ± 0.21 b 0.98 ± 0.1 b 10.32 ± 0.45 a
TN_perc 0.07 ± 0.01 b 0.04 ± 0 b 0.59 ± 0.01 a
TOC_perc 1.43 ± 0.19 b 0.88 ± 0.11 b 9.94 ± 0.47 a
WSOC_mg_g 1.04 ± 0.28 a 1 ± 0.16 a 0.67 ± 0.04 a
Ca_meq100g 3.52 ± 0.41 b 0.81 ± 0.11 c 17.21 ± 0.75 a
Mg_meq100g 1.08 ± 0.09 b 0.26 ± 0.03 b 7.56 ± 0.46 a
pH 5.31 ± 0.17 b 4.79 ± 0.11 c 5.82 ± 0.05 a
EC_dS_m 0.07 ± 0.01 a 0.06 ± 0.01 a NA
Sand_perc 31.8 ± 7.47 b 90.8 ± 1.74 a 14 ± 2.12 b
Silt_perc 53 ± 7.36 a 3.8 ± 0.66 b 58.5 ± 1.5 a
Clay_perc 15.2 ± 0.37 b 5.4 ± 1.29 c 27.75 ± 1.65 a

pore distribution

pore size distribution

pore size distribution

summary

pore_size = read.csv("processed/pore_size_perc_freq2.csv")

pore_size_summary = 
  pore_size %>% 
# make new bins
  dplyr::mutate(bins_um = case_when(pore_size==0 ~ "<100",
                                    pore_size>0&pore_size<1000~as.character(pore_size+100),
                                    pore_size>=1000&pore_size<2000 ~ "1000-2000",
                                    pore_size>=2000&pore_size<3000 ~ "2000-3000",
                                    pore_size>=3000&pore_size<4000 ~ "3000-4000")) %>% 
  group_by(bins_um) %>% 
  dplyr::summarise(CPCRW = round(sum(cpcrw),2),
                   DWP = round(sum(dwp),2),
                   SR = round(sum(sr),2)) %>% 
  knitr::kable()

WSOC tables

soils

mg/g soil

Treatment CPCRW DWP SR
Drought 0.85 ± 0.2 0.72 ± 0.37 0.6 ± 0.04
Field Moist 0.69 ± 0.07 0.35 ± 0.05 0.61 ± 0.05
Saturated 0.59 ± 0.05 0.39 ± 0.06 0.55 ± 0.06
Time Zero 0.78 ± 0.1 0.51 ± 0.08 0.43 ± 0.04

pores

mg/L

Treatment 1.5 kPa CPCRW 1.5 kPa DWP 1.5 kPa SR 50 kPa CPCRW 50 kPa DWP 50 kPa SR
Drought 111.82 ± 41.45 * 69.02 ± 13 13.9 ± 2.43 186.86 ± 62.65 * 69.97 ± 13.39 19.3 ± 8.32
Field Moist 21.22 ± 3.73 46.33 ± 11.94 18.68 ± 2.36 * 41.17 ± 12.67 52.43 ± 14.74 19.82 ± 2.93
Saturated 52.82 ± 13.98 42.08 ± 7.34 23.16 ± 3.29 * 63.4 ± 20.84 82.7 ± 16.52 24.07 ± 2.43 *
Time Zero 26.3 ± 2.78 76.68 ± 21.76 6.03 ± 0.57 29.14 ± 4.41 92.33 ± 22.22 7.28 ± 0.34

figure

wsoc = read.csv("processed/wsoc_pores_longform.csv")

ggplot(wsoc, aes(y = as.numeric(wsoc_mg_L), x = Site, color = Treatment))+
geom_point(position = position_dodge(width =0.5))+
facet_wrap(~Suction)
## Warning: Removed 12 rows containing missing values (geom_point).

pores

mg/g

Treatment 1.5 kPa CPCRW 1.5 kPa DWP 1.5 kPa SR 50 kPa CPCRW 50 kPa DWP 50 kPa SR
Drought 25.81 ± 12.78 NaN ± NA 2.81 ± 0.55 3.45 ± 2.06 NaN ± NA 0.54 ± 0.05
Field Moist 4.52 ± 1.73 4.02 ± 1.45 4.03 ± 0.95 0.9 ± 0.32 0.4 ± 0.08 0.96 ± 0.27
Saturated 19.7 ± 6.62 5.94 ± 2.58 5.25 ± 0.84 1.05 ± 0.28 0.58 ± 0.26 1.4 ± 0.36
Time Zero 5.1 ± 1.8 4.42 ± 1.98 1.46 ± NA 1.05 ± 0.24 1.26 ± 0.77 NaN ± NA

FTICR Soil – figures

native SOM

Van Krevelen plot for baseline soil

Van Krevelen plot for baseline soil

treatment effect – all peaks

Van Krevelen plot for treatments

Van Krevelen plot for treatments

treatment effect – unique peaks

Van Krevelen plot for unique peaks

Van Krevelen plot for unique peaks

peaks lost and gained

soil_lost = 
  soil_unique %>% 
  gather(treatment,loss, drought2:saturation2)

gg_vankrev(soil_lost,aes(x = OC, y = HC, color = loss))+
    scale_color_brewer(palette = "Dark2")+
  facet_grid(treatment~site)+
  guides(colour = guide_legend(override.aes = list(alpha = 1)))+
  theme_bw()+
  theme_kp()
## Warning: Removed 137370 rows containing missing values (geom_point).

treatment effect – relative abundance

Relative abundance

Relative abundance

NOSC

NOSC for CPCRW

NOSC for CPCRW

NOSC table

site baseline drought field moist saturation time zero saturation
CPCRW -0.3636 -0.3529 -0.3846 -0.4211 -0.4000
DWP -0.3200 -0.4545 -0.4444 -0.4211 -0.3871
SR -0.2963 -0.3333 -0.4444 -0.4286 -0.3750

lost/gained NOSC

gained = read.csv("fticr/fticr_pore_newmolecules.csv")

ggplot(gained, 
       aes(x = NOSC, fill = newmolecules, color = newmolecules))+
  geom_histogram(binwidth = 0.10, position = "identity", alpha = 0.2)+
  scale_fill_brewer(palette = "Dark2")+
  scale_color_brewer(palette = "Dark2")+
  # scale_fill_manual(values = c("#666666","#1B9E77", "#D95F02", "#7570B3"))+
  #  scale_color_manual(values = c("#666666","#1B9E77", "#D95F02", "#7570B3"))+
  #geom_histogram(data = subset(fticr_pore_nosc, site = "CPCRW" | treatment=="field moist"), fill = "red", alpha = 0.2)+

  xlim(-2.5, 2.5)+
  #ylim(0,1000)+

  facet_grid(site+tension~treatment)+ #facet with two variables
  
  theme_bw()+
  theme_kp()
## Warning: Removed 4 rows containing non-finite values (stat_bin).
## Warning: Removed 72 rows containing missing values (geom_bar).

ggplot(gained, 
       aes(y = NOSC, x = treatment, color = newmolecules))+
  geom_boxplot(position = position_dodge(), fill = "white", lwd = 1,fatten = 1)+ # fatten changes thickness of median line, lwd changes thickness of all lines
 # geom_dotplot(binaxis = "y",position = position_dodge(0.75),                stackdir = "center", dotsize = 0.1, color = "black")
  facet_grid(site~tension)

aromatic peaks

aromatic peaks

aromatic peaks

FTICR Soil – tables

relative abundance

site Class baseline drought field moist saturation time zero saturation
CPCRW AminoSugar 5.46 ± 0.16 4.06 ± 0.11 * 4.63 ± 0.1 * 5.66 ± 0.11 5.53 ± 0.15
CPCRW Carb 6.16 ± 0.16 4.57 ± 0.06 * 5.53 ± 0.1 * 6.64 ± 0.12 * 6.72 ± 0.12
CPCRW Lipid 9.3 ± 0.34 9.8 ± 0.17 11.16 ± 0.23 * 12.22 ± 0.2 * 11.43 ± 0.28
CPCRW Protein 14.68 ± 0.24 13.48 ± 0.19 * 14 ± 0.13 15.67 ± 0.17 * 15.44 ± 0.3
CPCRW UnsatHC 4.1 ± 0.27 5.4 ± 0.17 * 5.2 ± 0.08 * 4.89 ± 0.07 * 4.82 ± 0.04
CPCRW ConHC 14.62 ± 0.43 16.89 ± 0.38 * 15.51 ± 0.1 12.99 ± 0.23 * 13.8 ± 0.26
CPCRW Lignin 34.91 ± 0.48 35.65 ± 0.18 34.32 ± 0.32 32.26 ± 0.23 * 32.77 ± 0.32
CPCRW Tannin 6.91 ± 0.29 6.24 ± 0.08 * 5.69 ± 0.08 * 5.86 ± 0.12 * 5.84 ± 0.1
CPCRW Unnamed 3.84 ± 0.11 3.91 ± 0.05 3.96 ± 0.02 3.81 ± 0.07 3.65 ± 0.1
DWP AminoSugar 7.31 ± 0.12 4.1 ± 0.07 * 4.7 ± 0.05 * 4.08 ± 0.09 * 5.21 ± 0.15
DWP Carb 9.92 ± 0.05 4.64 ± 0.07 * 6.07 ± 0.08 * 5.51 ± 0.12 * 6.92 ± 0.12
DWP Lipid 9.31 ± 0.08 13.72 ± 0.22 * 13.65 ± 0.09 * 13.85 ± 0.3 * 12.05 ± 0.47
DWP Protein 14.49 ± 0.11 16.29 ± 0.19 * 16.48 ± 0.14 * 13.77 ± 0.16 * 14.04 ± 0.06
DWP UnsatHC 4.19 ± 0.03 5.58 ± 0.05 * 4.75 ± 0.05 * 6.23 ± 0.07 * 5.47 ± 0.16
DWP ConHC 12.17 ± 0.22 16.38 ± 0.09 * 15.19 ± 0.23 * 16.71 ± 0.3 * 15.82 ± 0.12
DWP Lignin 31.74 ± 0.17 31.48 ± 0.19 31.57 ± 0.19 31.03 ± 0.25 31.49 ± 0.32
DWP Tannin 7.03 ± 0.05 4.07 ± 0.09 * 4.15 ± 0.11 * 4.91 ± 0.1 * 5.11 ± 0.15
DWP Unnamed 3.84 ± 0.04 3.74 ± 0.07 3.44 ± 0.1 * 3.91 ± 0.07 3.89 ± 0.04
SR AminoSugar 3.41 ± 0.13 4.64 ± 0.08 * 5.26 ± 0.22 * 5.13 ± 0.11 * 4.18 ± 0.06
SR Carb 5.33 ± 0.03 5.48 ± 0.11 6.56 ± 0.45 * 6.63 ± 0.38 * 4.94 ± 0.19
SR Lipid 12.02 ± 0.23 10.06 ± 0.26 * 12.78 ± 0.21 12.45 ± 0.15 12.79 ± 0.17
SR Protein 12.17 ± 0.47 14 ± 0.39 * 14.93 ± 0.48 * 15.35 ± 0.19 * 14.15 ± 0.2
SR UnsatHC 5.76 ± 0.13 5.37 ± 0.13 6.32 ± 0.13 * 6.16 ± 0.1 6.61 ± 0.11
SR ConHC 20.77 ± 0.76 17.19 ± 0.19 * 12.84 ± 0.89 * 13.3 ± 0.48 * 17.08 ± 0.3
SR Lignin 31.98 ± 0.26 33.9 ± 0.42 * 33.08 ± 0.16 * 32.68 ± 0.22 30.76 ± 0.31
SR Tannin 5.24 ± 0.13 5.28 ± 0.12 4.7 ± 0.06 * 4.74 ± 0.08 * 5.1 ± 0.04
SR Unnamed 3.32 ± 0.06 4.09 ± 0.05 * 3.53 ± 0.08 3.56 ± 0.16 4.38 ± 0.15

peaks

site Class baseline drought field moist saturation time zero saturation
CPCRW AminoSugar 495 431 523 587 540
CPCRW Carb 554 472 603 675 655
CPCRW Lipid 914 1056 1304 1353 1226
CPCRW Protein 1313 1366 1528 1627 1527
CPCRW UnsatHC 427 608 618 577 544
CPCRW ConHC 1358 1684 1711 1379 1376
CPCRW Lignin 3032 3414 3596 3237 3156
CPCRW Tannin 587 603 598 585 563
CPCRW Unnamed 385 428 463 436 391
CPCRW total 9065 10062 10944 10456 9978
DWP AminoSugar 742 435 291 448 615
DWP Carb 994 476 369 589 774
DWP Lipid 990 1462 841 1555 1447
DWP Protein 1480 1653 982 1458 1615
DWP UnsatHC 461 632 308 716 683
DWP ConHC 1272 1649 923 1787 1839
DWP Lignin 3215 3088 1868 3191 3551
DWP Tannin 706 425 259 523 596
DWP Unnamed 406 415 223 454 486
DWP total 10266 10235 6064 10721 11606
SR AminoSugar 247 552 625 607 427
SR Carb 373 631 776 755 495
SR Lipid 835 1220 1546 1505 1316
SR Protein 836 1618 1725 1782 1406
SR UnsatHC 410 651 767 770 687
SR ConHC 1368 2000 1503 1621 1737
SR Lignin 2111 3758 3702 3651 2951
SR Tannin 362 607 536 547 510
SR Unnamed 252 504 448 461 481
SR total 6794 11541 11628 11699 10010
## uniqu e peaks
site Class drought field moist saturation
CPCRW AminoSugar 153 161 166
CPCRW Carb 150 190 209
CPCRW Lipid 513 659 674
CPCRW Protein 469 484 552
CPCRW UnsatHC 289 284 298
CPCRW ConHC 634 650 440
CPCRW Lignin 965 1012 844
CPCRW Tannin 157 147 149
CPCRW Unnamed 202 221 210
DWP AminoSugar 147 72 140
DWP Carb 125 71 184
DWP Lipid 734 271 832
DWP Protein 627 194 515
DWP UnsatHC 329 123 386
DWP ConHC 620 236 735
DWP Lignin 962 323 1043
DWP Tannin 151 58 189
DWP Unnamed 222 84 251
SR AminoSugar 196 171 172
SR Carb 211 184 168
SR Lipid 541 690 647
SR Protein 517 522 521
SR UnsatHC 257 319 332
SR ConHC 851 511 632
SR Lignin 1110 966 964
SR Tannin 246 147 175
SR Unnamed 253 211 221

aromatic peaks

# soil_aromatic_summary %>% 
#   ungroup %>% 
#   dplyr::mutate(counts = paste(counts_mean,dunnett)) %>% 
#   dplyr::select(treatment, site, counts) %>% 
#   spread(site, counts) %>% 
#   knitr::kable()

Shannon diversity

soil_shannon_summary %>% 
  dplyr::mutate(H = paste(round(H_mean,2), "\U00B1", round(H_se,2), dunnett),
                H = str_replace_all(H, "NA","")) %>%
  dplyr::select(treatment, site, H) %>% 
  spread(site, H) %>% 
  knitr::kable()
treatment CPCRW DWP SR
baseline 1.92 ± 0.01 1.99 ± 0 1.9 ± 0.01
drought 1.89 ± 0 * 1.91 ± 0 * 1.92 ± 0
field moist 1.92 ± 0.01 1.93 ± 0 * 1.94 ± 0 *
saturation 1.96 ± 0 * 1.95 ± 0 * 1.95 ± 0 *
time zero saturation 1.95 ± 0 1.96 ± 0 1.95 ± 0

Respiration

figures

Time series of CO2 flux

ggplot(flux_data, aes(y = CO2_flux_mgC_gSoil_hr, x = inctime_hours, color = Treatment))+
  geom_point()+
  facet_grid(Treatment~Site)
## Warning: Removed 499 rows containing missing values (geom_point).

Flux by site/treatment

ggplot(flux_data, aes(y = CO2_flux_mgC_gSoil_hr, x = Site, color = Treatment))+
  geom_point(position = position_dodge(width=0.5))
## Warning: Removed 499 rows containing missing values (geom_point).

flux summary table

flux_summary_table = 
  flux_summary %>% 
  gather(variable, value,MEAN_CO2_FLUX_ugC_gSoil_hr:CUM_CO2_FLUX_mgC_gSoil) %>% 
  dplyr::mutate(var = paste(variable, Site)) %>% 
  dplyr::select(Treatment, var,value) %>% 
  spread(var, value) %>% 
  knitr::kable()
## Warning: attributes are not identical across measure variables;
## they will be dropped